Department of Drug Discovery and Development, Harrison College of Pharmacy, Auburn University, 3306 Walker Building, Auburn, Alabama 36849, United States.
Department of Computer Science and Software Engineering, Auburn University, Auburn, Alabama 36849, United States.
J Chem Inf Model. 2022 Dec 26;62(24):6378-6385. doi: 10.1021/acs.jcim.2c00307. Epub 2022 Aug 10.
Secondary metabolites from natural sources are promising starting points for discovering and developing drug prototypes and new drugs, as many current treatments for numerous diseases are directly or indirectly related to such compounds. Recent advances in bioinformatics tools and molecular networking methods have made it possible to identify novel bioactive compounds. In this study, a workflow combining network-based methods for identifying bioactive compounds found in natural products was streamlined by innovating an automated bioinformatics software. The workflow relies on Global Natural Product Social Molecular Networking (GNPS), a web-based mass spectrometry ecosystem that aims to be an open-access knowledge base for community-wide organization and sharing of raw, processed, or annotated fragmentation mass spectrometry data. By combining computational tools including MZmine2, GNPS, and Cytoscape, the integrated dashboard quickly creates bioactive molecular networks with minimal user intervention and reduces the processing time of the original workflow by over 80%. This newly automated workflow quickens the process of discovering bioactive compounds from natural products. This study uses extracts from leaves to demonstrate the application of our automated software.
天然来源的次生代谢产物是发现和开发药物原型和新药的有前途的起点，因为许多目前治疗许多疾病的方法直接或间接地与这些化合物有关。生物信息学工具和分子网络方法的最新进展使得识别新型生物活性化合物成为可能。在这项研究中，通过创新自动化生物信息学软件，简化了结合基于网络的方法识别天然产物中生物活性化合物的工作流程。该工作流程依赖于全球天然产物社会分子网络（GNPS），这是一个基于网络的质谱生态系统，旨在成为一个开放获取的知识库，用于社区范围内组织和共享原始、处理或注释的碎片质谱数据。通过结合 MZmine2、GNPS 和 Cytoscape 等计算工具，集成仪表板可以快速创建具有最小用户干预的生物活性分子网络，并将原始工作流程的处理时间减少 80%以上。这个新的自动化工作流程加快了从天然产物中发现生物活性化合物的过程。本研究使用叶提取物来演示我们自动化软件的应用。
